Bibliographic record
Abstract
Ultraviolet (UV) technology holds promise as a low cost non-thermal alternative to heat pasteurization of liquid foods and beverages. However, its application for foods is still limited due to low UV transmittance (LUVT). LUVT foods have a diverse range of chemical (pH, Brix, Aw), physical (density and viscosity) and optical properties (absorbance and scattering) that are critical for systems and process designs. The commercially available UV sources tested for foods include low and medium pressure mercury lamps (LPM and MPM), excimer and pulsed lamps (PUV). The LPM and excimer lamps are monochromatic sources whereas emission of MPM and PUV is polychromatic. The optimized design of UV-systems and UV-sources with parameters that match to specific product spectra have a potential to make UV treatments of LUVT foods more effective and will serve its further commercialization. In order to select UV source for specific food application, processing effects on nutritional, quality, sensorial and safety markers have to be evaluated. This paper will review current status of UV technology for food processing along with regulatory requirements. Discussion of approaches and results of measurements of chemico-physical and optical properties of various foods (fresh juices, milk, liquid whey proteins and sweeteners) that are critical for UV process and systems design will follow. Available UV sources did not prove totally effective either resulting in low microbial reduction or UV over-dosing of the product thereby leading to sensory changes. Beam shaping of UV light presents new opportunities to improve dosage uniformity and delivery of UV photons in LUVT foods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".